These models and simulations have been tagged “BMA 708”.
Model of Covid-19 outbreak in Burnie, Tasmania
This model was designed from the SIR
model(susceptible, infected, recovered) to determine the effect of the covid-19
outbreak on economic outcomes via government policy.
The government policy is triggered when the
number of infected is more than ten.
The government policies will take a negative
effect on Covid-19 outbreaks and the financial system.
We set some fixed and adjusted variables.
Covid-19 outbreak's parameter
Fixed parameter: Background
Adjusted parameters: Infection rate, recovery rate. Immunity loss rate can be
changed from vaccination rate.
Government policy's parameters
Adjusted parameters: Testing rate(from 0.15
to 0.95), vaccination rate(from 0.3 to 1), travel ban(from 0 to 0.9), social
distancing(from 0.1 to 0.8), Quarantine(from 0.1 to 0.9)
Fixed parameter: Tourism
Adjusted parameter: Economic growth rate(from
0.3 to 0.5)
An increased vaccination rate and testing
rate will decrease the number of infected cases and have a little
more negative effect on the economic system. However, the financial system
still needs a long time to recover in both cases.
This is a system dynamic model to
describe relationship between local logging industry and biking tourism in
Tasmanian Derby Mountain.
In the dynamic model, the left-hand side shows how Derby
get income from local biking tourism. The biking visitors number are influenced
by scenery evaluation which depend on local size of forest and influenced government policy support when Biking Tourism income
is over 1000 unit. Biking visitors with good recommendation will also back to
Mountain Derby and bring income for local in twice or more times. In the right-hand side, we found the income of
logging industry was influenced by local logging growth rate and government
policy if local Biking Tourism income is over 1000 unit. The increase of
logging industry will also increase local employment which will influence employee
cost. This factor will also affect total logging income in Derby Mountain.
The simulation results show, with governments support the
Biking tourism will increase sharply in the first few years and finally instead
local logging industry, at same time bring good environment and save local
forest under local increase logging industry. The recommendation graph shows
that, the number of good recommendation & bad recommendation for Derby
Mountain biking tourism will also increase in high speed in front of few years
with data fluctuation but finally maintain in a stable line. Last simulation
graph shows that how policy factor influences logging and biking industry. The Government
has strong support in local tourism, however, as number of tourists increase,
the positive impact from government support will continue decrease. On the contrary,
the government support influence will also decease to local logging industry when
logging been instead by tourism.